Simultaneous Design of FIR Filter Banks and Spatial Patterns for EEG Signal Classification
Simultaneous Design of FIR Filter Banks and Spatial Patterns for EEG Signal Classification
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DOI：
10.1109/tbme.2012.2215960
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发表时间：
2013-04-01
影响因子：
4.6
通讯作者：
Tanaka, Toshihisa
中科院分区：
文献类型：
--
作者：
Higashi, Hiroshi;Tanaka, Toshihisa
在基于运动图像的脑机接口(MI-BCI)中，电极的空间权重被称为公共空间模式(CSP)，被认为是脑电信号分类的有效方法。为了在CSP中实现准确的分类，有必要找到与BCI任务相关的大脑活动的频段。已经提出了几种确定这样一组频带的方法。然而，现有的方法不能仅利用学习数据来寻找多个频段。为了解决这个问题，我们提出了区分滤波器组CSP(DFBCSP)，它通过优化一个目标函数来设计有限冲激响应滤波器和相关的空间权重，该目标函数是CSP的自然扩展。通过将原问题分成多个子问题的顺序交替求解来进行优化。实验表明，DFBCSP能有效地提取MI-BCI的鉴别特征。实验结果表明，DFBCSP算法能够检测和提取运动图像中与脑活动相关的频段。
The spatial weights for electrodes called common spatial pattern (CSP) are known to be effective in EEG signal classification for motor imagery-based brain-computer interface (MI-BCI). To achieve accurate classification in CSP, it is necessary to find frequency bands that relate to brain activities associated with BCI tasks. Several methods that determine such a set of frequency bands have been proposed. However, the existing methods cannot find the multiple frequency bands by using only learning data. To address this problem, we propose discriminative filter bank CSP (DFBCSP) that designs finite impulse response filters and the associated spatial weights by optimizing an objective function which is a natural extension of that of CSP. The optimization is conducted by sequentially and alternatively solving subproblems into which the original problem is divided. By experiments, it is shown that DFBCSP can effectively extract discriminative features for MI-BCI. Moreover, experimental results exhibit that DFBCSP can detect and extract the bands related to brain activities of motor imagery.